Int. J. Mol. Sci. 2011, 12, 2982-2993; doi:10.3390/ijms12052982

International Journal of

Molecular Sciences ISSN 1422-0067

www.mdpi.com/journal/ijms

Article

3D-QSAR Studies on a Series of Dihydroorotate Dehydrogenase

Inhibitors: Analogues of the Active Metabolite of Leflunomide

Shun-Lai Li *, Mao-Yu He and Hong-Guang Du *

College of Science, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang

District, Beijing 100029, China; E-Mail: wulianhmy2008@163.com (M.-Y.H.)

* Authors to whom correspondence should be addressed; E-Mails: lishunlai@yahoo.com.cn (S.-L.L.);

dhg@mail.buct.edu.cn (H.-G.D.); Tel.: +86-010-60871527.

Received: 7 March 2011; in revised form: 31 March 2011 / Accepted: 28 April 2011 /

Published: 10 May 2011

Abstract: The active metabolite of the novel immunosuppressive agent leflunomide has

been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme

catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular

field analysis (SOMFA), a simple three-dimensional quantitative structure-activity

relationship (3D-QSAR) method is used to study the correlation between the molecular

properties and the biological activities of a series of analogues of the active metabolite. The

statistical results, cross-validated rCV2 (0.664) and non cross-validated r

2 (0.687), show a

good predictive ability. The final SOMFA model provides a better understanding of

DHODH inhibitor-enzyme interactions, and may be useful for further modification and

improvement of inhibitors of this important enzyme.

Keywords: DMARDs design; DHODH inhibitors; 3D-QSAR; SOMFA

1. Introduction

The dihydroorotate dehydrogenase (DHODH) ia an essential mitochondrial enzyme that catalyzes

the flavin mononucleotide-dependent formation of orotic acid, a key step in de novo pyrimidine

biosynthesis [1,2]. This enzyme is an attractive chemotherapeutic target in various pathogens, such as

Plasmodium falciparum and Helicobacter pylori, and for the treatment of human disease, such as

cancer, malaria and rheumatoid arthritis [3–5].

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All potent inhibitors of DHODH published to date bind to the putative ubiquinone binding channel

and display beneficial immunosuppressive and antiproliferative activities, shown to be most pronounced

during T-cell proliferation [6]. Brequinar and leflunomide are two examples of low-molecular weight

inhibitors of DHODH that have been in clinical development [7–9]. Leflunomide is now marketed as a

treatment for rheumatoid arthritis. A series of analogues of the active metabolite of an

immunosuppressive agent leflunomide have also been synthesized and found to inhibit DHODH [10].

Quantitative structure-activity relationships are the most important applications of chemometrics,

giving useful information for the design of new compounds acting on a specific target. Quantitative

structure-activity relationship (QSAR) attempts to find a consistent relationship between biological

activity and molecular properties. Thus, QSAR models can be used to predict the activity of new

compounds. Although there has been much interest in synthesis of various inhibitors of DHODH, there

have been few QSAR studies of DHODH inhibitors [10–13]. Kuo [10] and Ren [11] have even reported

the structure-activity relationships (SAR) and quantitative structure-activity relationship (2D-QSAR) of

this series of analogues, respectively.

The self-organizing molecular field analysis (SOMFA) [14] is a simple 3D-QSAR technique, which

has been developed by Robinson et al. The method has similarities to both comparative molecular field

analysis (CoMFA) [15] and molecular similarity studies. Like CoMFA, a grid-based approach is used;

however, SOMFA only uses steric and electrostatic maps, which are related to interaction energy maps,

no probe interaction energies need to be evaluated. The weighting procedure of the grid points by

Mean-Centered-activity is an important ingredient of the SOMFA procedure. Like the similarity

methods, it is the intrinsic molecular properties, such as the molecular shape and electrostatic potential,

which are used to develop the QSAR models.

A SOMFA model could suggest a method of tackling the all-important alignment, which all

3D-QSAR methods have faced. The inherent simplicity of this method allows the possibility of

aligning the training compounds as an integral part of the model derivation process and of aligning

prediction compounds to optimize their predicted activities.

In a recent study, leflunomide has been found to exhibit some dose-dependent side effects in a small

number of patients [16]. The purpose of this paper is to describe the application of self-organizing

molecular field analysis, SOMFA, on the analogues of the active metabolite of leflunomide, to analyze

the three-dimensional quantitative structure-activity relationships (3D-QSAR) and to determine the

structural requirements of this series of analogues for optimum activity. The 3D-QSAR together with

the modeling studies will provide a more precise elucidation of the molecular forces involved in the

DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement

of inhibitors of this important enzyme.

2. Materials and Methods

2.1. Data Sets

The biological activities of analogues of the active metabolite of leflunomide were taken from the

papers by Kuo et al. [10]. Not every compound from Kuo’s paper was included in the 3D-QSAR

analysis because of the lack of parameters (6 compounds) and the exact IC50 values (IC50 105 nM for

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6 compounds). All analogues were classified into two subgroups according to the substituents at their

two different positions, 42 aromatic substituted analogues and 12 side chain 3-substituted analogues.

Fifty-three analogues are divided into two sets. The training set of 42 molecules with structures are

shown in Table 1 and their enzyme inhibitory activities expressed as log(1/IC50) are shown in

Section 3. The predictive power of the models is evaluated using a test set of 11 molecules whose

structures are also shown in Table 1 and activities will be shown in Section 3.

Table 1. Chemical structures of active metabolite analogues of leflunomide.

NH

O OH

N

R1

R2 R3

Compd

No. R1 R2 R3

Compd

No. R1 R2 R3

1 H H H 29 Cl CH3 H

2 CH3 H H 30* Cl H CH3

3 CF3 H H 31 CH3 Cl H

4 H CF3 H 32 Br CH3 H

5* Cl H H 33 CN CH3 H

6 H Cl H 34 CF3S CH3 H

7 H H Cl 35* CF3O CH3 H

8 Br H H

36

S

H H 9 CN H H

10* -CH2CN H H

11 CF3S H H

37 O

H H 12 CF3SO H H

13 CF3SO2 H H

14 CH3S H H

38 C

O

Cl

H H 15* CH3SO H H

16 CH3SO2 H H

17 CF3O H H

39 Cl

H H 18 CH3O H H

19 OH H H

20* NO2 H H

40*

H2C

H2C

H H 21 H2N H H

22 CH3CO H H

23 H2NCO H H

41 F

H H 24 HOOC- H H

25* CH3O2C- H H

26 CF3 CH3 H

42

H H 27 CF3 C2H5 H

28 C2F5 CH3 H

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Table 1. Cont.

NH

R

O OH

N

F3C

Compd

No. R

Compd

No. R

43 -CH3 48

3

49

44

50*

45*

51

46 O

52 CH3

47

O

53*

*-Test set.

Two sets of molecules are selected in order to elucidate convenient models for the predictive

discrimination between these various activities.

2.2. Molecular Modeling and Alignment

The three-dimensional structures of the analogues were constructed with the ArgusLab 4.01 [17]

according to the conformations of active metabolite A771726 (Compound 43) from PDB 1d3h [18],

running on an AMD Athlon 64 X2 Dual Core Processor 3600 + CPU/Microsoft Win XP platform.

Unless otherwise indicated, parameters are default. Full geometry optimizations are performed first

by molecular mechanics MM2, and then optimized by PM3 semi-empirical method in the ArgusLab

software. The final active conformations are then performed RMS overlapping and fitted with the

compound 43 as a reference. Two different alignments are selected to define overlap. The atom

numbers and corresponding sequence for each alignment are defined in Table 2.

Table 2. The atom numbers and three-atom sequences defining the two alignments

(compound 43 is used to define the atom number).

NH

CH3

O OH

N

F3C1 2

3

4 5

Alignment No. 1st atom 2nd atom 3rd atom

1 1 2 3

2 2 4 5

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According to two alignment of the final active conformations of analogues, these compounds are

then performed SOMFA analysis. The superposition of active analogues structures according to

alignment 1 (considering phenyl ring similarity) has been shown in Figure 1, the superposition of

active analogues according to the alignment 2 (considering all skeleton similarity) has also been shown

in Figure 2. Using VEGA software [19], the final overlayed geometries are converted into CSSR file

format, the only file format which the SOMFA2 program can accept to process a SOMFA analysis.

Figure 1. Superposition of active analogues structures according to alignment 1.

Figure 2. Superposition of active analogues structures according to alignment 2.

2.3. SOMFA 3D-QSAR Models

In the SOMFA study a 40 × 40 × 40 Å grid originating at (−20, −20, −20) with a resolution of 0.5 Å,

is generated around the aligned compounds, and all compounds have been assigned charges by the

MNDO hamiltonian semi-empirical method according to our previous works [20,21]. 12 different

models using different enzyme, compound subgroups and alignment under exploration are presented in

Table 3.

For all of the studied compounds, shape and electrostatic potential are generated. To sum up the

predictive power of these two properties into one final model, we combine their individual predictions

using a weighted average of the shape and electrostatic potential based QSAR, using a mixing

coefficient (c1) as illustrated in Equation 1 [14].

Activity = c1Activityshape + (1 − c1)ActivityESP (1)

Clearly, multiproperty predictions could have been obtained through multiple linear regression.

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Using Equation 1 instead gives greater insight into the resultant model by allowing the study of the

variation in predictive power with different values of c1.

With the highest value of r2, the SOMFA models then are derived by the partial least squares (PLS),

implemented in SPSS software [22] with cross-validation.

The predictive ability of the model is quantitated in terms of rCV2 which is defined in Equation 2.

rCV2 = (SD − PRESS)/SD (2)

where PRESS = (Ypred − Yactual) and SD = (Yactual − Ymean).

SD is the sum of squares of the difference between the observed values and their meaning and

PRESS is the prediction error sum of squares. The final models are constructed by a conventional

regression analysis with the optimum value of mixing coefficient (c1) equal to that yielding the highest

r2 and rCV

2 value according to Equation 2.

3. Results and Discussion

SOMFA, a novel 3D-QSAR methodology, is employed for the analysis with the training set

composed of 42 various compounds, from which biological activities are known. Statistical results of

12 SOMFA models are summarized in Table 3.

Table 3. Statistics of the various SOMFA models.

Rat DHODH Mouse DHODH

All analogues Aromatic substituted

analogues

Side chain

3-substituted

analogues

All analogues

Aromatic

substituted

analogues

Side chain

3-substituted

analogues

Align. 1 2 1 2 1 2 1 2 1 2 1 2

r2 0.658 0.687 0.758 0.665 0.778 0.897 0.517 0.572 0.554 0.657 0.697 0.859

rCV2 0.636 0.664 0.739 0.641 0.715 0.865 0.485 0.545 0.516 0.620 0.610 0.822

F 98.428 112.251 125.687 79.723 35.180 87.269 52.608 65.608 47.310 72.892 23.106 61.314

s 0.651 0.623 0.528 0.621 0.604 0.412 0.657 0.619 0.628 0.551 0.590 0.402

c1 0.695 0.766 0.650 0.769 0.800 0.987 0.531 0.625 0.429 0.660 0.934 0.918

rpred2 0.818 0.717 0.571 0.549 0.972 0.981 0.657 0.679 0.512 0.693 0.993 0.991

r2, Non cross-validated correlation coefficient; rCV2, Cross validated correlation coefficient; F, F-test value; s, standard

error of estimate; c1, mixing coefficient of SOMFA model; rpred2, Predictive r2.

A cross-validated value rCV2 which is obtained as a result of PLS analysis serves as a quantitative

measure of the predictability of the SOMFA model. We find that the quality of the QSAR model was

dependent upon the alignment and number of molecules. The model overlayed using alignment 2

shows higher rCV2

values than using the model of alignment 1, and the model of subgroups shows

higher rCV2

values than the model of all analogues.

Among the models tested from all analogues, good cross-validated correlation coefficient rCV2

values (0.664), moderate non-cross-validated correlation coefficient r2 values (0.687) proves a good

conventional statistical correlation which have been obtained, and we also find that the resultant

SOMFA model have a satisfying predictive ability.

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The observed and predicted activities of the training set are reported in Table 4. Figure 3 shows a

satisfying linear correlation and moderate difference between observed and predicted values of

molecules in the training set.

Table 4. Observed and predicted activities of 42 compounds in the training set.

Compd

Rat DHODH Mouse DHODH

log(1/IC50) log(1/IC50)

Observed Predicted Residuala Observed Predicted Residual

a

1 5.699 5.846 −0.146 5.801 5.888 −0.088

2 6.631 6.454 0.175 6.541 6.182 0.358

3 7.678 7.783 −0.103 7.328 7.385 −0.055

4 6.320 6.873 −0.553 6.280 6.545 −0.265

6 6.465 6.772 −0.312 5.523 6.447 −0.927

7 4.876 5.421 −0.541 4.780 5.501 −0.721

8 7.102 6.474 0.625 7.444 6.336 1.103

9 7.276 7.383 −0.103 7.377 7.114 0.265

11 8.301 8.067 0.232 7.000 6.723 0.277

12 7.796 6.736 1.063 6.380 5.957 0.422

13 8.523 8.018 0.501 7.051 6.885 0.164

14 7.886 7.509 0.380 6.352 6.350 −0.002

16 6.801 6.923 −0.124 4.821 6.026 −1.206

17 8.301 7.754 0.545 6.762 6.681 0.079

18 6.730 6.254 0.475 5.429 5.684 −0.254

19 5.100 6.006 −0.906 − − −

21 4.660 5.428 −0.768 4.500 5.275 −0.776

22 7.167 5.759 1.410 5.420 5.450 −0.030

23 4.851 5.292 −0.442 − − −

24 5.830 6.012 −0.182 5.429 5.703 −0.273

26 7.854 7.290 0.559 7.260 6.698 0.561

27 7.398 7.617 −0.217 7.149 6.984 0.165

28 7.959 8.423 −0.463 6.550 7.119 −0.567

29 7.4819 7.104 0.375 7.400 6.697 0.703

31 7.1029 7.043 0.056 6.550 6.543 0.007

32 7.3019 7.260 0.039 7.201 6.813 0.387

33 7.553 6.803 0.746 7.444 6.875 0.564

34 7.959 7.718 0.241 6.750 6.729 0.021

36 6.200 6.092 0.107 5.599 5.299 0.300

37 6.530 6.312 0.217 6.201 5.678 0.521

38 6.229 7.021 −0.791 6.250 6.438 −0.188

39 4.750 5.469 −0.720 5.301 5.380 −0.081

41 5.670 5.640 0.030 5.070 5.090 −0.020

42 5.830 6.735 −0.905 5.801 5.856 −0.055

43 7.886 7.906 −0.016 7.161 7.431 −0.270

44 6.550 6.402 0.147 6.680 6.274 0.406

46 4.750 5.791 −1.041 4.932 5.664 −0.732

47 4.350 5.299 −0.950 5.100 5.488 −0.388

48 6.530 6.261 0.268 7.036 6.210 0.826

49 6.600 6.371 0.229 6.710 6.276 0.434

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Table 4. Cont.

Compd

Rat DHODH Mouse DHODH

log(1/IC50) log(1/IC50)

Observed Predicted Residuala Observed Predicted Residual

a

51 6.301 6.505 −0.205 5.680 6.153 −0.473

52 5.851 6.039 −0.189 5.370 5.743 −0.373 a Residual = Observed − predicted.

Figure 3. Observed versus predicted activities (Rat DHODH) in the training set.

It is well known that the best way to validate a 3D-QSAR model is to predict biological activities

for the compounds forming the test set. The SOMFA analysis of the test set composed of

11 compounds is reported in Table 5. Most of the compounds in the test set show satisfying correlation

between observed and predicted values in Figure 4. We find that two compounds of test set

(compound 10 and 15) always have large residuals, and could be classified as outliers. This is true for

both rat and mouse DHODH models, there may be more complicated structure-activity relationships in

these two compounds. The statistical parameters rpred2 of test compounds excluding compound 10 and 15

are also summarized in Table 3; all the models performed well (rpred2 > 0.5) in the activity prediction of

most test compounds.

SOMFA calculation for both shape and electrostatic potentials are performed, then combined to get

an optimal coefficient c1 = 0.766 according to Equation 1. The master grid maps derived from the best

model is used to display the contribution of electrostatic potential and shape molecular field. The

master grid maps give a direct visual indication of which parts of the compounds differentiate the

activities of compounds in the training set under study. The master grid also offers an interpretation as

to how to design and synthesize some novel compounds with much higher activities. The visualization

of the potential master grid and shape master grid of the best SOMFA model is showed in Figure 5 and

Figure 6 respectively, with compound 43 as the reference.

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Table 5. Observed and predicted activities of 11 compounds in the test set.

Compd

Rat DHODH Mouse DHODH

log(1/IC50) log(1/IC50)

Observed Predicted Residuala Observed Predicted Residual

a

5 7.201 6.882 0.318 7.444 6.569 0.871

10 5.343 6.928 −1.588 4.429 6.185 −1.755

15 6.080 7.117 −1.037 4.650 6.340 −1.690

20 7.678 7.376 0.304 7.301 6.998 0.302

25 6.801 6.593 0.207 5.951 6.084 −0.134

30 5.903 5.710 0.190 5.429 5.794 −0.364

35 7.745 8.034 −0.294 6.750 6.847 −0.097

40 6.750 6.632 0.118 6.201 5.895 0.305

45 4.500 5.313 −0.813 4.550 5.392 −0.842

50 7.638 6.461 1.179 6.750 6.086 0.664

53 6.971 6.298 0.672 7.201 6.174 1.026 a Residual = Observed − predicted.

Figure 4. Observed versus predicted activities (Rat DHODH) in the test set.

Figure 5. The electrostatic potential master grid with compound 43, red represents areas

where postive potential is favorable, or negative charge is unfavorable, blue represents

areas where negative potential is favorable, or postive charge is unfavorable. (a) Rat

DHODH and (b) Mouse DHODH.

(a)

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Figure 5. Cont.

(b)

Figure 6. The shape master grid with compound 43, red represents areas of favorable steric

interaction; blue represents areas of unfavorable steric interaction. (a) Rat DHODH and

(b) Mouse DHODH.

(a)

(b)

Each master grid map is colored in two different colors for favorable and unfavorable effects. In

other words, the electrostatic features are red (more positive charge increases activity, or more negative

charge decreases activity) and blue (more negative charge increases activity, or more positive charge

decreases activity), and the shape feature are red (more steric bulk increases activity) and blue (more

steric bulk decreases activity), respectively.

It can be seen from Figure 5 and Figure 6 that the electrostatic potential and shape master grid for

Rat DHODH are very similar to that for Mouse DHODH. Because Rat DHODH have structural

similarities to Mouse DHODH, so active analogues have the same or a similar 3D-QSAR to them.

SOMFA analysis result indicates the electrostatic contribution is of a low importance (c1 = 0.766).

In the map of electrostatic potential master grid, we find a high density of blue points around the

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substituent R1 at the phenyl ring, which means some electronegative groups are favorable. Meanwhile,

the SOMFA shape potential for the analysis is presented as master grid in Figure 6. In this map of

important features, we can find a high density of red points around the substituent R1 and R2 at the

phenyl ring, which means a favorable steric interaction; simultaneously, we also find a high density of

blue points outside substituent R at the 3-substituted side chain, where an unfavorable steric interaction

may be expected to enhance activities. Generally, the medium-sized electronegative potential

substituent R1 and R2 (benzene ring with electron-withdrawing groups, pyridine ring, for example) at

the phenyl ring increases the activity, the small-sized substituent R (methyl, ethyl, for example) at the

3-substituted side chain increases the activity.

All analyses of SOMFA models may provide some useful information in the design of new active

metabolite analogues of leflunomide.

4. Conclusions

We have developed predictive SOMFA 3D-QSAR models for analogues of the active metabolite of

Leflunomide as anti-inflammatory drugs. The master grid obtained for the various SOMFA models’

electrostatic and shape potential contributions can be mapped back onto structural features relating to the

trends in activities of the molecules. On the basis of the spatial arrangement of the various electrostatic

and shape potential contributions, novel molecules are being designed with improved activity.

Acknowledgements

We gratefully acknowledge Daniel Robinson and the Computational Chemistry Research Group

(Oxford University, UK) for the use of the SOMFA software.

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